FURI | Fall 2023

Deep Gaussian Process and Reinforcement Learning Approach to Adaptive Maintenance Scheduling in Semiconductor Manufacturing

This study’s objective is to examine the viability of an adaptive maintenance system in semiconductor manufacturing using Deep Gaussian Processes (DGPs) and Reinforcement Learning (RL). This innovative approach could revolutionize maintenance procedures, offering proactive solutions that increase equipment reliability and reduce operational downtimes, providing substantial benefits to the semiconductor industry. The research may also open doors to broader manufacturing optimizations, leveraging machine learning synergies. Future endeavors should further validate the system’s applicability across diverse manufacturing environments and refine real-time adaptation mechanisms.